Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Empowering community partners in health disparities research: Refining a community-based participatory research (CBPR) training curriculum.

Pedagogy in health promotion·2026

Same author

Cross-sectional analysis of chikungunya vaccine acceptability among residents of Ponce, Puerto Rico, 2024-2025.

Vaccine·2026

Same author

Utility of sentinel surveillance as an early warning system for emerging pathogens in a low-resource setting: Correlations of dengue, chikungunya, Zika, and COVID-19 trends between sentinel and passive surveillance systems in Puerto Rico, 2012-2023.

PLOS global public health·2026

Same author

Leveraging Sentinel Enhanced Surveillance to Characterize the 2024-2025 Dengue Epidemic in Puerto Rico: Insights and Comparisons With Passive Surveillance.

Public health reports (Washington, D.C. : 1974)·2026

Same author

Increase in Travel-Associated and Locally Acquired Dengue Cases - United States, 2024.

MMWR. Morbidity and mortality weekly report·2026

Same author

Evaluation of Effectiveness of Autocidal Gravid Ovitraps for Preventing Zika Virus Infection, Puerto Rico, USA.

Emerging infectious diseases·2026

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Machine Learning for Improved Dengue Diagnosis in Puerto Rico.

Zachary J Madewell¹, Dania M Rodriguez¹, Maile B Thayer¹

¹Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico.

Tropical Medicine & International Health : TM & IH

|September 16, 2025

Summary

Machine learning models, particularly XGBoost and LightGBM, significantly improve dengue diagnosis using common clinical data. These advanced models offer a promising, accessible tool for accurate dengue detection, especially in resource-limited areas.

Keywords:

Caribbean dengue diagnosis extreme gradient boosting machine learning

More Related Videos

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Published on: March 13, 2018

Visualizing Dengue Virus through Alexa Fluor Labeling

Visualizing Dengue Virus through Alexa Fluor Labeling

Published on: July 9, 2011

Related Experiment Videos

Last Updated: Jan 17, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Published on: March 13, 2018

Visualizing Dengue Virus through Alexa Fluor Labeling

Visualizing Dengue Virus through Alexa Fluor Labeling

Published on: July 9, 2011

Area of Science:

Infectious Disease Epidemiology
Medical Informatics
Machine Learning in Healthcare

Background:

Accurate dengue diagnosis is challenging in resource-limited settings due to symptom overlap and diagnostic method limitations.
Existing diagnostic tools for dengue fever may not be readily accessible or rapid enough for widespread clinical use.

Purpose of the Study:

To develop and evaluate machine learning models for improved dengue diagnosis using accessible clinical data.
To enhance diagnostic accuracy for dengue, providing a potential rapid and accessible tool for healthcare providers.

Main Methods:

Utilized data from the Sentinel Enhanced Dengue Surveillance System (SEDSS) in Puerto Rico (May 2012-June 2024).
Evaluated various machine learning models (XGBoost, LightGBM, logistic regression, random forest, SVM, ANN, adaptive boosting) using demographic, clinical, laboratory, and epidemiological variables.
Assessed model performance via the area under the receiver operating characteristic curve (AUC).

Main Results:

XGBoost and LightGBM models achieved the highest diagnostic accuracy, with AUCs over 90%.
Key predictors for improved dengue diagnosis included monthly incidence, leukopenia, thrombocytopenia, rash, age, and absence of nasal discharge.
Incorporating comprehensive clinical and epidemiological features consistently enhanced model sensitivity and specificity.

Conclusions:

Machine learning models, especially XGBoost and LightGBM, demonstrate significant potential for accurate dengue diagnosis using readily available clinical data.
These models can be particularly valuable in resource-limited settings for improving dengue detection.
Future work should focus on developing user-friendly tools (e.g., mobile apps, web platforms) for clinical implementation and exploring predictive applications.